期刊
GEOPHYSICAL JOURNAL INTERNATIONAL
卷 221, 期 1, 页码 586-602出版社
OXFORD UNIV PRESS
DOI: 10.1093/gji/ggaa019
关键词
Electrical resistivity tomography (ERT); Downhole methods; Inverse theory; Numerical modelling; Time-series analysis
资金
- National Key Research and Development Plan [2016YFC0401805, 2016YFC0401801, 2016YFC0801604]
- National Program on Key Basic Research Project of China [2014CB046901, 2015CB058101]
- National Key Scientific Instrument and Equipment Development Project [51327802]
- National Natural Science Foundation of China [51739007, 51479104, 41502279]
- Royal Academy of Engineering under the UK-China Industry Academia Partnership Programme [UK-CIAPP\314]
- Shandong Province Key Research and Development Plan [2016ZDJS02A01]
- Henan Province Major Science and Technology Special Projects program [161100211100]
- Shandong University Fundamental Research Fund [2017JC002]
4-D electrical resistivity tomography (ERT), an important geophysical method, is widely used to observe dynamic processes within static subsurface structures. However, because data acquisition and inversion consume large amounts of time, rapid changes that occur in the medium during a single acquisition cycle are difficult to detect in a timely manner via 4-D inversion. To address this issue, a scheme is proposed in this paper for restructuring continuously measured data sets and performing GPU-parallelized inversion. In this scheme, multiple reference time points arc selected in an acquisition cycle, which allows all of the acquired data to be sequentially utilized in a 4-D inversion. In addition, the response of the 4-D inversion to changes in the medium has been enhanced by increasing the weight of new data being added dynamically to the inversion process. To improve the reliability of the inversion, our scheme uses actively varied time-regularization coefficients, which arc adjusted according to the range of the changes in model resistivity; this range is predicted by taking the ratio between the independent inversion of the current data set and historical 4-D inversion model. Numerical simulations and experiments show that this new 4-D inversion method is able to locate and depict rapid changes in medium resistivity with a high level of accuracy.
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